Summary of A Rescaling-invariant Lipschitz Bound Based on Path-metrics For Modern Relu Network Parameterizations, by Antoine Gonon et al.
A rescaling-invariant Lipschitz bound based on path-metrics for modern ReLU network parameterizations
by Antoine Gonon, Nicolas Brisebarre, Elisa Riccietti, Rémi Gribonval
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a novel approach to establishing Lipschitz bounds on neural network parameterizations. This is crucial for ensuring generalization, quantization, or pruning guarantees, as it controls the robustness of the network with respect to parameter changes. The proposed bound is intrinsically invariant with respect to rescaling symmetries of the networks and is broadly applicable to modern architectures such as ResNets, VGGs, and U-nets. This work sharpens previously known bounds and provides a new perspective on Lipschitz analysis in neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to measure how well a type of computer program, called a neural network, can change when some of its parts are adjusted. Neural networks are used to recognize things like pictures or speech. To make sure these programs work correctly, we need to understand how they behave when small changes are made. The authors came up with a new way to measure this behavior, which is important for making sure neural networks continue to work well even if some parts change. |
Keywords
» Artificial intelligence » Generalization » Neural network » Pruning » Quantization
